class_docstr: Cifar10 data set.
  
  Described by http://www.cs.toronto.edu/~kriz/cifar.html.
docstr: Read the images and labels from 'filenames'.
docstr_tok: ["Read","the","images","and","labels","from","'filenames","'","."]
code: def make_batch(self, batch_size):
      """Read the images and labels from 'filenames'."""
      filenames = self.get_filenames()
      # Repeat infinitely.
      dataset = tf.data.TFRecordDataset(
          filenames
      ).repeat()  # CHANGE_ARJUNA
      # dataset = tf.contrib.data.TFRecordDataset(filenames).repeat() #OLD: FOR AUTON CLUSTER
  
      # Parse records.
      dataset = dataset.map(  # CHANGE_ARJUNA
          self.parser, num_parallel_calls=batch_size
      )
      # dataset = dataset.map(                                       #OLD: FOR AUTON CLUSTER
      # self.parser, num_threads=batch_size, output_buffer_size=2 * batch_size)
  
      # Potentially shuffle records.
      if self.subset == "train":
          min_queue_examples = int(
              Cifar10DataSet.num_examples_per_epoch(self.subset) * 0.4
          )
          # Ensure that the capacity is sufficiently large to provide good random
          # shuffling.
          dataset = dataset.shuffle(
              buffer_size=min_queue_examples + 3 * batch_size
          )
  
      # Batch it up.
      dataset = dataset.batch(batch_size)
      iterator = dataset.make_one_shot_iterator()
      image_batch, label_batch = iterator.get_next()
  
      return image_batch, label_batch
  
file_path: dragonfly/examples/nas/cg/cifar/cifar10.py
imports: import os
  import tensorflow
label_desc: Use image node_id 4 for calling the Cifar10DataSet obj's underlying member method code with example usage: obj.make_batch(batch_size) and returns: image_batch,  label_batch